
TextRank Proposed by [1]}, TextRank harnesses the PageRank algorithm to choose the sentences with the highest similarity scores to the original document.For this paper we utilized the TextRank package from Summa NLP: https://github.com/summanlp/textrank

KLSum An algorithm introduced by [2]} which greedily selects the sentences that minimize the Kullback-Lieber (KL) divergence between the original text and proposed summary.

Lead-1 A common baseline in news summarization is to select the first 1-3 sentences of the original text as the summary [3]}. With this dataset, we include the first sentence as the summary as it is the closest to the average number of sentences per reference (1.2).

Lead-K A variation of Lead-1, this baseline selects the first k sentences until a word limit is satisfied.

Random-K This baseline selects a random sentence until a word limit is satisfied. For this baseline, the reported numbers are an average of 10 runs on the entire dataset.

